data, analytics, & ai: how trust delivers value · analytics practices to incorporate ai-based...

ON BEHALF OF:

Findings From the Annual Data & Analytics Global Executive Study

Data, Analytics, & AI: How Trust Delivers Value

C U S T O M R E S E A R C H R E P O R T

CUSTOM RESEARCH REPORT — DATA, ANALYTICS, AND AI: HOW TRUST DELIVERS VALUE

Table of ContentsExecutive Summary .......................................................................................................................................................................................1

Section I: Building Trust in Data: How Analytics Leaders Get ‘the Right Stuff’ ...................................................................................2

• Trust Advances Analytics Maturity ......................................................................................................................................................2• Why Closing the Trust Gap Matters .................................................................................................................................................... 3

• Grade Your Data ................................................................................................................................................................................... 4

• The Human Factor: Partner With Domain Experts ............................................................................................................................ 5

• AI Built on a Bedrock of Data Governance ........................................................................................................................................ 5 • Commit to Treating Data as an Asset ................................................................................................................................................. 6

Section II: Success With Customer Data Depends on Keeping Customers’ Trust .........................................................................10

• The Pivotal Roles of Data Security and Privacy ................................................................................................................................10

• The Opportunity to Build Customer Trust Based on Data ................................................................................................................11

• Trust Is Fragile — Handle With Care ...................................................................................................................................................11

Section III: Building Trust in Innovation by Creating a Culture of Inquiry and Experimentation ......................................................15

• Driving Data Literacy Through the Workforce ..................................................................................................................................16

• Fostering Collaboration Drives Culture Change ...............................................................................................................................16

• Analytics Expertise: Centralize vs. Decentralize .............................................................................................................................. 17

• Communication and Education Encourage an Analytics Mindset ..................................................................................................18

Leaders’ Best Practices

• Health Care – Cleveland Clinic’s Centralized Data Store Helps Build Trust in Analytics ..................................................................... 8 • Manufacturing – Caterpillar Tailors Analytics Strategies to Business Unit Needs .......................................................................... 9

• Government – DataSF Teaches the Art of Asking Analytical Questions ........................................................................................13

• Financial Services – Cross-functional Teamwork Improves Predictive Models at Barclays US ...................................................14

About the Research ....................................................................................................................................................................................20

Acknowledgments ......................................................................................................................................................................................20

Sponsor’s Viewpoint ....................................................................................................................................................................................21

MIT SMR Connections develops content in collaboration with our sponsors. It operates independently of the MIT Sloan Management Review editorial group.

Copyright © Massachusetts Institute of Technology, 2019. All rights reserved.

1

Deriving more value from analytics and emerging technologies

like artificial intelligence starts with trust, simply because data

collected for analytics must be trusted. Much like the need for

sterility in clinical laboratories or a clear chain of evidentiary

custody in law enforcement, customers and partners that share

data must trust that it’s safeguarded and used appropriately from

collection through storage and to how it’s applied. Conversely,

once insights emerge from applying analytics to the data, indi-

viduals throughout the organization must understand the care

given to data management so that they trust those insights —

and use them — to make decisions and ask new questions.

Our global survey of more than 2,400 business leaders and man-

agers provides insight into organizations’ activities in each of

these key areas and identifies where recognized best practices

are becoming more mainstream and where they may still be

exceptional. It found that respondents who have advanced their

analytics practices to incorporate AI-based technologies such

as machine learning and natural language processing work in

organizations that do the most to foster data quality, safeguard

data assets, and develop cultures of data literacy and innovation.

Overall, the survey found a persistent gap: While the majority

of respondents report increased access to data, only a minority

say they have the “right” data to make decisions. Notably, those

who report a high level of trust in data for analytics are also

more likely to show leadership in foundational activities that

ensure that the data is high quality and leads to useful insights.

The survey, augmented by interviews with leading practitioners

and experts in the field, gauges the data analytics practices

of organizations around the world and how respondents view

the effectiveness of those practices. Three major conclusions

emerge among the chief findings:

1. Better data governance is needed.

A minority of respondents have formal activities in data quality

assurance, which points to the need for a greater commitment

to data governance in support of advanced analytics. The prac-

titioners interviewed provide examples of approaches to data

architecture, governance, and quality that can build trust in

data for analytics.

2. Data privacy emerges as an opportunity.

Data security is the strongest focus among survey respondents,

but there are opportunities to increase the maturity of security

practices by applying analytics and AI in this arena. Data

privacy initiatives are not quite as strong. Interviews highlight

opportunities to approach privacy initiatives and GDPR as a

way to build trust with customers, rather than simply treating

them as compliance-driven mandates.

3. Fostering an analytics culture improves innovation.

Leadership and management practices that support a culture

of analytics-driven innovation are relatively strong, but the

research shows many organizations have an opportunity to do

more to spread the necessary skills and mindset throughout the

workforce. A minority of respondents are engaged in activities

that develop workforce data literacy; meanwhile, finding a

workforce with the right skills was cited as one of the most sig-

nificant challenges to innovating. Interviews with practitioners

show how analytics leaders can foster a culture of innovation

by educating, communicating, and collaborating with partners

in lines of business.

Organizational choices — such as centralizing the analytics

function and having a chief data officer or chief analytics

officer role — may also help advance analytics maturity. Those

who have a CDO or CAO are more likely to report that they

have the data needed for decision-making, as are those who

work in organizations where analytics are centralized.

For leaders of organizations still striving to achieve analytics

maturity, the testimony from practitioners — who range

from data leaders at multinational corporations to the head

of a small municipal government team — is particularly

useful. Their chief lesson: Communication and collabora-

tion between analytics and business experts lead to mutual

understanding and measurable benefits. In other words,

trust delivers value.l

Executive Summary


2

Building Trust in Data: How Analytics Leaders Get ‘the Right Stuff’

When enterprise information leaders at the Cleveland

Clinic set the goal of advancing the organization’s

data analytics maturity about four years ago, they

had already established strong programs for decision support

and business intelligence. They knew that going beyond dash-

boards and reports to give clinicians and managers predictive

— and prescriptive — insights powered by artificial intelligence

and machine learning would demand more than just supplying

the technology.

The leading academic medical center recognized that to create

a culture where people understand and use advanced analytics

to make better decisions, it needed to focus on its information

and data: “Ensure that it’s available, that it’s valid, that it’s

governed appropriately, that we have the right processes

around accessing it, using it, sharing it, protecting it,” says

Chris Donovan, executive director of enterprise information

management and analytics.

Trust Advances Analytics Maturity

Like other leading organizations pursuing advanced analytics

capabilities, the Cleveland Clinic has placed a priority on

building trust — trust in the data that’s collected and stored

and trust in the analytic insights it generates. And it has seen

how building that trust can reinforce a culture that trusts and

embraces data-driven decision-making.

Findings from our recent survey, which focused on the data

and analytics practices of more than 2,400 business leaders

and managers, underscores the importance of such priorities.

We found a strong correlation between those who report

using the most advanced analytics techniques and those whose

organizations actively foster data quality, safeguard data

assets, and build a data-driven culture.

These organizations focus on data quality and management.

They set up measures for governing its proper use and security,

and by following these practices, they achieve results. In the case

of the Cleveland Clinic, that means more researchers trust the

data they are accessing from a centralized data lab instead of

copying data to work on in their own, siloed system. This leads to

more consistent data — and more precise results, Donovan says.

However, such benefits, bred from best analytics practices, are

still not widespread: Our research shows that most organiza-

tions are still developing their analytics capabilities. Just 15%

of survey respondents report that they use advanced analytics

to inform management decisions. Fewer than one in 10 are

working with automated analytics, and only 7% apply machine

learning and artificial intelligence in decision-making or

production workflows. Far more common, respondents rely

on business intelligence tools and employee dashboards to

support decision-making.

At the same time, we observed what could be called a “utility

gap”: While 76% of respondents report they have increased

access to data they judge useful — which is not surprising, given

the proliferation of data that accompanies the digitalization of

business — that access does not equal empowerment. A much

smaller number say they are able to leverage that data: Only

43% feel they frequently have the right data needed to make

decisions. This utility gap is a persistent trend, with a similar

gap found in the MIT Sloan Management Review survey in 20171

(see Figure 1).

While the majority of survey respondents reported increased access to data in surveys conducted in 2017 and 2018, those who believe they have the data they need for decision-making remain in the minority.

Figure 1: A ‘Utility Gap’ Persists

1 S. Ransbotham and D. Kiron, “Using Analytics to Improve Customer Engagement,” MIT Sloan Management Review, Jan. 30, 2018.

78% 76%

44% 43%

2017 2018

Percentage of respondents reporting somewhat or significantly improved access to useful data over the past year

Percentage of respondents reporting frequently or always having the right data to informbusiness decisions

Percentage of respondents reporting somewhat or significantly improved access to useful data over the past year

Percentage of respondents reporting frequently or always having the right data to informbusiness decisions


3

Why Closing the Trust Gap Matters

While there can be a range of reasons why people may not feel

they have the “right” data to handle, our survey probed and

found evidence for one: a trust gap. Only a very small minority

of respondents say they “always” trust data judged by qualities

of relevance, completeness, timeliness, and accuracy; slightly

more than half say they judge data trustworthy by those qualities

at least “often” (see Figure 2). This finding suggests a significant

opportunity to shore up data quality to build confidence in data

for analytics, in particular when it comes to increasing the

likelihood that data is complete — the aspect trusted least often.

There is ample opportunity for organizations to do more:

Only 21% of survey respondents report formal approaches

to data quality, which we defined as routinely monitoring,

managing, and improving data quality as part of a formal data

governance effort (see Figure 3). The largest group is reactive

to quality issues — a practice that Jeanne Ross, principal

research scientist at the MIT Center for Information Systems

Research, advises against.

“The worst place to fix the data is when it’s already been

collected,” says Ross. Data quality efforts should focus on the

business process that takes in data, whether that is from cus-

tomers or a part of the business.

Ross acknowledges such pragmatism takes commitment. While

it’s straightforward to say, “Fix your processes so that the data

collection is very reliable and the quality issues are pretty min-

imal,” meeting that goal is a challenge. That’s because it takes

ongoing discipline to refine data collection processes, testing

data quality regularly along the way.

But in her research, Ross has found the effort pays off. “Here’s

the interesting thing about analytics: Your unique opportunity

is going to be on your own data,” she says. The principle applies

whether a company is using its own internal data or augment-

ing that data with third-party sources. “If you have data, and

you supplement that data and you do that in ways that other

Few survey respondents are always confident in the quality of their analytics data, although a majority of respondents often trust that it’s accurate, up to date, and relevant. Trust in completeness of data is lowest, but trust in accuracy is most frequent.

Percentages may not equal 100 due to rounding.

Informal: Individuals who produce or use data reactively correct for accuracy, consistency, timeliness, and completeness

Data stewardship: Someone is responsible for proactively identifying and correcting causes of data quality problems

7%21%

30% 42%

Formal: Data quality is routinely monitored, managed, and improved as part of a formal data governance e�ort

No data quality e�orts

Just one in five organizations takes a formal approach to data quality, while 30% report at least proactive efforts. The plurality of respondents still tackle the issue informally.

Figure 3: Data Quality Efforts Show Room for Improvement

Figure 2: Data Accuracy Is Most Trusted Quality

How often do you trust that analytics data is:

40%

43%

Always

Relevant Complete Up to date Accurate

Often Sometimes Rarely Never

6%

1%

6%

28%

42%

21%

3%

12%

44%

34%

9%

1%

9%

47%

37%

6%

1%

11%

Always Often Sometimes Rarely Never

Informal: Individuals who produce or use data reactively correct for accuracy, consistency, timeliness, and completeness Data stewardship: Someone is responsible for proactively identifying and correcting causes of data quality problems

Formal: Data quality is routinely monitored, managed, and improved as part of a formal data governance effort

No data quality efforts


companies can’t, once you understand the data, once you get

insights, you’re able to do things other companies couldn’t.”

Grade Your Data

Our survey respondents recognize that data quality begins at

home: Internal data is most often subject to verification, fol-

lowed by customer data (see Figure 4). While that may lead to

the high degree of trust in internal data, customer data — key

to many advanced analytics applications — still lags in fourth

place when it comes to the degree of trust placed in it.

Practitioners and experts interviewed for this report note that

as more companies drive to build advanced analytics capa-

bilities and the volume of available data continues to expand,

organizations are looking for ways to include new informa-

tion sources in analytics systems. To do so effectively requires

building ways to mitigate the risk of bad data getting baked

into processes and driving faulty conclusions.

“The desire to do advanced analytics is driving this appetite for

more data, which makes people go out and pull data from other

places,” says David Loshin, principal consultant at Knowledge

Integrity. “Without instituting information governance prac-

tices, they’re at risk of not achieving the goals that they set out

to achieve.”

Trustworthy analytics requires setting up policies that assert

the standard for confidence levels in the data an organization

will use, determining the provenance of the data, and estab-

lishing acceptable data use, Loshin says. Statistical analysis and

technology tools can help identify problems and clean up data

sets (or lead to decisions not to use them).

Toronto-based Sun Life Financial generally begins with the

premise that no data is trustworthy, regardless of source, be-

cause all data has quality issues. Still, how data will be used also

factors into how trustworthy it needs to be, says Eric Monteiro,

senior vice president of client solutions at the global financial

services company. “We do hold different bars for different

types of use cases. For example, for general client segmenta-

tion or even business cases where we are sizing an opportunity

and making a strategic decision, the bar for quality is lower

because in general, the errors will even out up and down, and

you’re OK in the end,” he says. However, if data is used to drive

14%

42%

Regularly

Data from sensors/IoT

Sometimes Never

44%

62%

5%

33% 32%

51%

18%

39% 39%

21%

Internally generated

data

Publicly available

data

Regulators’data

34%

49%

18%

Competitors’data

42%48%

11%

Vendor-provided

50%

42%

9%

Customer-provided

4%

39%

Trusted

Data from sensors/IoT

Somewhat trusted Not trusted

57%

63%

2%

35%

23%

66%

11%

55%

40%

5%

Internally generated

data

Publicly available

data

Regulators’data

12%

67%

21%

Competitors’data

29%

64%

7%

Vendor-provided

37%

58%

5%

Customer-provided

Figure 4: Verify and Trust

How often do you verify: How much do you trust insights based on:

Most attention is paid to verifying internal and customer data. Internal data is also the most trusted source, while that provided by customers lags in fourth place.


Regularly Sometimes Never Trusted Somewhat trusted Not trusted


4

individual communications with clients — sending an email

based on the assumption of a certain life event, for example —

there must be less tolerance for error.

“If we go to you and say, ‘Congratulations on retirement,’ and

it turns out even though you’re 64, you are not at all thinking

about retirement, that’s pretty embarrassing and a pretty bad

client experience,” Monteiro points out.

The Human Factor: Partner With Domain Experts

Understanding what data can be trusted may be easier when

analytics practitioners work closely with domain experts in

their organizations.

Joy Bonaguro, who oversaw a team of five as chief data officer

for the city and county of San Francisco (she has left the orga-

nization since being interviewed for this report), says ongoing

consultations with in-house clients make or break a project.

That’s precisely because project teams are often using data

that was not collected with modeling in mind, she says.

“We definitely have to do clean-up, but the way we manage

quality is we probably spend an unusual amount of time com-

pared to your average data science team on doing what we call

exploratory analysis briefings,” Bonaguro says. “That’s where

we’re really confirming with a client, ‘Are we looking at your data

correctly? Where are there data quality issues? How should we

treat those data quality issues during the modeling phase?’”

Teaming data scientists with domain experts and data experts

— who understand data sources and how they can be auto-

mated — should be a best practice in every analytics operation,

says Dean Abbott, co-founder and chief data scientist at

SmarterHQ, an analytics firm that works with marketers.

For example, if an online retailer is seeking to understand data

showing a spike in abandoned online shopping carts, people

with different expertise and perspective may note different

patterns and identify different resolution paths that may have

been missed without that collaboration. A retail expert would

expect certain shopping behavior patterns and check analyt-

ics results against assumptions and identify questions to ask

that are not part of the initial probe. On the other hand, a

data infrastructure expert would be able to spot and examine

anomalies arising from technical issues and might know which

adjustments to the model would help either zero in on those

anomalies or provide the insight to confidently disregard them.

AI Built on a Bedrock of Data Governance

Those seeking to shore up practices that enable a more robust

capability in analytics and AI might look to construction and

mining equipment giant Caterpillar for inspiration.

Caterpillar uses AI to help marketers predict when customers

are ready to purchase and signal when customers are likely to

stop buying. Product development engineers use AI to simulate

product performance before building prototypes and

Teaming data scientists with domain experts and data experts — who understand data sources and how they can be automated — should be a best practice in every analytics operation. DEAN ABBOTT, SMARTERHQ

5


separately simulate how an equipment operator will use a

machine in the field so they can anticipate customer needs.

Natural language processing automates the analysis of legal

documents, checking the validity of agreements and pinpoint-

ing issues that need attention; it is also used to analyze tens of

thousands of customer and dealer comments to predict future

quality and warranty issues.

“Thanks to AI, things that would have taken a person two to three

weeks to do manually we can do in 10 minutes,” says Morgan

Vawter, chief analytics director at Caterpillar.

Behind all of these capabilities lies a strong data management

foundation, Vawter says. Data governance ensures that the

right data gets used so that results are trustworthy. Ensuring

that the data is high quality and its provenance is clear lets

analytics professionals start with good building blocks for

their models. “We need to make sure we have a common

truth in the data, including the underlying data, how it’s been

processed, how it’s been analyzed, how diverse it is,” she

says. “And then, ultimately, we can take that and build more

accurate, scalable analytics.”

At the Workplace Safety and Insurance Board (WSIB) Ontario,

charting the government agency’s course to AI and advanced

analytics begins with data architecture. Like many in her

position, Christina Hoy, vice president of corporate business

information and analytics, finds that challenge made more

complex by many legacy systems and data.

“We have a lot of data that’s in disparate systems. Up to now, we

have not had a formal strategy. We want to approach this in a

thoughtful manner,” Hoy says. She is working to create “a better

data strategy and an architecture that will actually let us use the

information the way it should be to make better decisions.”

WSIB is strong at diagnostic and descriptive analytics, Hoy says,

but the agency sees the opportunity to do more to op-

erationalize predictive analytics. It has also convened an

exploratory AI working group. Before it can take advantage of

such advanced technologies, Hoy says, “we need to make sure

our data is well-structured. You can’t do it if the foundation is

held together with duct tape and staples.”

Hoy invests time in communicating and educating to gain sup-

port and budget for this critical effort from within the agency

and from management. “If they can’t see what I’m trying to

achieve, I won’t be successful,” she says. “They have to be able

to visualize as well as I can what I’m trying to achieve. I think

that’s my biggest objective — to communicate that vision.”

Hoy sometimes uses an iceberg analogy, likening the 10% float-

ing above the water to the analytics results that leaders need,

while “the 90% of the iceberg that floats under the water is my

data architecture.” And she gets results: “They understand we

actually have to have a data strategy to enable analytics and

decision-making.”

Commit to Treating Data as an Asset

While many corporate leaders agree that data is an import-

ant asset, those who back up that view with committed

organizational resources may be faster to gain advantage from

AI and advanced analytics. At Barclays US, a set of management

decisions to treat data as an asset underlies the success of such

efforts, according to Vishal Morde, vice president of data sci-

ence and advanced analytics. For example, his business unit’s

chief data officer reports directly to the CEO. And the bank es-

tablished a data management council to catalog all data assets,

each asset’s owner, and policies for who gets to use the data.

This governance structure creates internal understanding

about the importance of sound data management and en-

sures trust in the company’s data resources, Morde says. The

“Thanks to AI, things that would have taken a person two to three weeks to do manually we can do in 10 minutes.” MORGAN VAWTER, CATERPILLAR


6

approach is crucial for the consumer bank’s Data Science

Center of Excellence, where the focus is on reaching customers

with compelling offers while maintaining a business relation-

ship built on trust.

Morde’s team has used advanced techniques such as machine

learning, deep learning, natural language processing algo-

rithms, topic modeling, and sentiment analysis to examine

customer complaints. After finding patterns in this text-based

data, the bank changed some of its policies.

“The natural language processing allowed us to uncover those

deep, hidden insights. We were able to think about it very com-

prehensively from the customer’s point of view and actually

made some tangible impact on the customer experience,” Mor-

de says. After these changes, complaint rates dipped to their

lowest point in four years. And in the 2018 J.D. Power Credit

Card Satisfaction Study, Barclays US moved from seventh posi-

tion to third position.

As these leading practitioners acknowledge, a focus on data

quality requires committing resources and budget. Again, our

research found a fairly modest minority in the vanguard: Just

15% reported significant budget increases for data quality in

the past year (see Figure 5). While it’s good news that 40% still

saw some increase in budget, it’s likely that the remainder,

whose budgets stayed flat or decreased, may find it a bit more

challenging to advance their analytics maturity.

Better news may be that a full third of survey respondents now

work in organizations that employ a chief data officer or chief

analytics officer. We found that those respondents are signifi-

cantly more likely to also report being on the right side of the

utility gap — meaning they have the right data to inform their

business decisions.l

Data quality needs funding to back up the commitment: Only a relatively small per-centage of companies gave these efforts a markedly higher priority in budgets over the past year.

Figure 5: Putting Their Money Where Their Data Is

40%

15%

38%

5%

2%

Significantlyincreased

Somewhatincreased

Nochange

Somewhatdecreased

Significantlydecreased

7


8

Cleveland Clinic’s Centralized Data Store Helps Build Trust in Analytics

I N D U S T R Y S N A P S H O T

The leaders of Cleveland Clinic’s 4-year-old enterprise analytics initiative are focused on building trust in the data the organization makes available to support decisions. Creating a central platform is one strategy to advance those efforts.

Before the effort began, Cleveland Clinic had a very decentralized approach, with teams building their own data stores and developing their own analytics projects with inconsistent results.

A centralized analytics platform was also about establishing one set of data for the organization, says Chris Donovan, executive director of enterprise information management and analytics. “If we make that platform compelling enough in terms of performance, and really partner with them and educate them in terms of the data that we have available, we eliminate that need to copy data all over the place and people begin to trust that central data store,” Donovan says.

The approach is working. Teams from around the clinic are moving some of their work into the platform, which is designed to enable them to have administrative rights to the data in a dedicated area of what Donovan calls a data lab. A team needs approval to create its space in the lab but not to do analytics work there.

There are some big benefits. First, teams that use the centralized platform are no longer asking Donovan’s group for copies of clinic data sets to experiment with. Second, the platform improves the quality of the data because people accessing it don’t have to worry whether they are getting the most up-to-date version of the data — that’s known. And third, the platform enhances the culture for analytics.

“Those very tangible changes in behavior indicate to me that we’re building that trust,” Donovan says.

Because people are using the central data store, Donovan’s team now has insight into updates and modifications that are made to the data. “We can have a conversation about, ‘Is this really the data that’s wrong? Is this an interpretation issue?’” he says. That kind of awareness can help clear up confusion about differing uses of terminology and lead to agreement about data definitions. By winning trust in a central data store, the clinic enabled a feedback loop that can lead to another benefit: better data management.

HE

ALT

H C

AR

E

Chris Donovan, executive director of

enterprise information management and analytics,

Cleveland Clinic

Those very tangible changes in behavior indicate to me that we’re building that trust.”“


9

“We have to make sure that we’re not skipping key parts of the journey,” Vawter explains. “You can’t just skip descriptive analytics, building dashboards, understanding data, and jump right to predictive and prescriptive analytics. We want to make sure that we’re helping them to understand their data at the foundation and then advance them up the maturity curve.”

With an overarching approach that provides a global view of all the company’s efforts, Caterpillar can take successful projects and apply them throughout the company. Previously, analytics excellence may have sprung up in pockets with efforts such as supply chain analytics or marketing analytics, but those tools may not have then been applied through the business unit. “Our analytics road maps have really showed the power of, ‘Hey, if I’m doing analytics here, it impacts this and it impacts the entire value chain.’ And so we’ve seen a lot of scaled analytics as a result of having those strategies,” Vawter says.

MA

NU

FAC

TU

RIN

G

Morgan Vawter, chief analytics director,

Caterpillar


Caterpillar Tailors Analytics Strategies to Business Unit Needs

To build a culture of data-driven decision-making and innovation, Caterpillar’s analytics group works closely with business unit leaders throughout the $45 billion company. This helps ensure that analytics supports business needs and wins executive support.

Morgan Vawter, chief analytics director at the company, says her team sits down with the leaders of units to tailor strategies to fit the needs of each. “It’s making sure that, because we have a very diverse business, we don’t just have an analytics strategy for the company — we have analytics strategies that enable the business unit strategies,” Vawter says. “Also, that’s useful because it gets that executive buy-in. They become advocates for it, and that cascades down and really creates a level of ownership in the business unit for using analytics to enable business success.”

Caterpillar serves industry segments including energy, transportation, construction, and mining, and has a unit for customer and dealer support. Ensuring that each group has its own analytics strategy enables Vawter’s organization to tailor analytics projects to maximize value for each unit — and to fit its analytics maturity level.

We want to make sure that we’re helping them to understand their data at the foundation and then advance them up the maturity curve.”

“


10

Success With Customer Data Depends on Keeping Customers’ Trust

Practitioners in our recent Data & Analytics survey have

gotten the memo on data security: Analytics depend

on data — and if that data is lost or stolen, or if cus-

tomers and partners become reluctant to share data because

of concerns over how it will be handled, the data-driven enter-

prise is at risk.

Common-sense security practices are widespread among sur-

vey respondents. A strong majority — 63% — either have or

are implementing a response plan in case their organization

suffers a data breach. Seven out of 10 either track or are cre-

ating the means to track where sensitive data is stored. Sim-

ilar majorities keep or are planning to create updated lists of

sensitive data they collect and train all employees in IT security

practices (see Figure 6).

The Pivotal Roles of Data Security and Privacy

Even more sophisticated measures are in place or underway

at most organizations. A slight majority have implemented or

are planning to deploy advanced analytics to predict cyber-

intrusion risks. More than half (54%) are using or implementing

a cybersecurity framework. And 37% have a chief information

security officer to lead these efforts, with another 18% plan-

ning or implementing that role (see Figure 7).

Analytics practitioners need to be aware of the environment

in which they do business. Even companies in the busi-

ness-to-business market that are not tied to negative publicity

— think of consumer data breaches or controversies about

the way social media networks manage user data — must note

customers’ heightened concerns about data use. “When

19%

Currently do this

Have responseplan in place

in case of data breach

Implementing Considering

44%

12%14%

Track where all data is stored

Keep updated list of sensitive

data types collected

Train all employees in IT

security risks and practices

Planning No activity

11%

47%

23%

11%10%10%

43%

20%

13% 13%11%

Currently do this

Apply advanced analytics to

predict cyber-intrusion risks


22%

16%15%

Use cybersecu-rity frameworks (e.g., PCI, NIST)

Employ a chief information

security o�cer


33%

39%

15%

11%

37%

7%

13%

44%

20%

12%13%

11%

14%16%

20%

11%

33%

Figure 6: Data Breach Defenses Are Up

Figure 7: Security Frameworks and CISOs Take Hold

Organizations are moving toward solid, baseline data security practices, although many have yet to fully implement these measures.


A slight majority of survey respondents are increasing their data security maturity via implementation of security frameworks, and nearly half have or are hiring a CISO. A minority are using more sophisticated measures such as applying analytics and AI to security.


19%

Currently do this

Have responseplan in place

in case of data breach


44%

12%14%

Track where all data is stored

Keep updated list of sensitive

data types collected

Train all employees in IT

security risks and practices


11%

47%

23%

11%10%10%

43%

20%

13% 13%11%

Currently do this

Apply advanced analytics to

predict cyber-intrusion risks


22%

16%15%

Use cybersecu-rity frameworks (e.g., PCI, NIST)

Employ a chief information

security o�cer


33%

39%

15%

11%

37%

7%

13%

44%

20%

12%13%

11%

14%16%

20%

11%

33%

Currently do this Implementing Planning Considering No activity



11

consumers see bad news stories out there about things that

happen, that can have a negative, spiraling effect on other

companies and other industries,” says Morgan Vawter, chief

analytics director at Caterpillar.

“We can’t do any of this work without trust with our customers

and the businesses that we serve,” Vawter adds. “And so we’re

making sure that we have clearly established rights to use the

information and ensuring that that lineage and that right and

that permission is very clear with every type of data that we

use and every data source that we access.”

While organizations are pursuing data security with some

urgency, their efforts on privacy lag. Just 41% of respondents

say they keep customers informed about data collection and

have internal controls in place. (see Figure 8).

The Opportunity to Build Customer Trust Based on Data

While some may view privacy regulations such as the Europe-

an Union General Data Protection Regulation (GDPR) as added

bureaucratic obligations, others see them as an opportunity to

build trust with customers. Susan Etlinger, an industry analyst

at the Altimeter Group, falls into the latter camp.

Etlinger observes that both business leaders and consumers

are becoming more aware of the many ways their data is used

and beginning to understand the impact that data use has on

their lives, their businesses, and society. As such, the widely

publicized 2018 compliance deadline for the GDPR caught

the attention of most organizations, with more than half

saying at the time of the survey that they had either finished

with compliance, were working on it, or were planning to work

on it (see Figure 9).

“This is actually a tremendous moment to reimagine what

data-centric organizations should look like and what the cus-

tomer experience should be,” she says. While organizations

must act in accordance with their fiduciary responsibilities

to comply with regulations, business leaders can also start to

imagine a world in which respecting privacy and building trust

works to their advantage.

In this environment, companies that figure out ways to use

data that provide value and enhance the trust of their custom-

ers will gain an edge. Given the option “to bank with a company

where you don’t know where your data’s going and you don’t

know how it’s being used or [to do business] with one who says,

‘You know, we actually want a partnership with you and we

want to be able to offer you the best possible experience, and

to do that, here’s what we ask and this is your choice,’” people

will be more inclined to choose the second, Etlinger says.

Trust Is Fragile — Handle With Care

Among leading analytics practitioners, there are a number

who strive to create such trusting relationships. As might be

expected, many can be found in industries like financial

41% Notify customers how we collect, use, and share their information, and have internal controls over how employees use the data

20%

25%

14%

Notify customers how we collect, use, and share their information

Implemented data privacy measures but have not yet communicated them externally

It’s not an issue we are concerned with

16%

27%

14%

25%

18%

We are fully compliant with GDPR

We are actively working on GDPR compliance

We are planning to comply with GDPR

GDPR is not a requirement but may guide our privacy policy

We have no plans for compliance

Privacy efforts lag security efforts, with just 41% keeping customers informed about data collection and use practices and also having internal controls in place.

Figure 8: Privacy Controls Have Room to Grow

Figure 9: GDPR Has Gained Attention

41% Notify customers how we collect, use, and share their information, and have internal controls over how employees use the data

20%

25%

14%




16%

27%

14%

25%

18%




GDPR is not a requirement but may guide our privacy policy


GDPR has the attention of most survey respondents, with more than half saying they have finished or are planning or working on compliance.

Notify customers how we collect, use, and share their information, and have internal controls over how employees use the data







GDPR is not a require-ment but may guide our privacy policy



12

services and health care. While these are, of course, governed

by stiffer regulations, success in these sectors has long

depended on earning and retaining deep trust, whether with a

customer’s life savings or a patient’s health.

At Sun Life Financial, data management leaders view GDPR as

a reinforcement of their existing practices, says Eric Monteiro,

senior vice president of client solutions. The financial services

company has a chief privacy officer, data breach notification

protocols, and a privacy impact assessment — all elements

required under GDPR. The company also has embarked on a

“plain and simple language initiative” that in the past year has

reviewed 500 standard letters customers receive with an eye

toward improving their clarity and minimizing legalese. The

prime reason for these activities — maintaining customer con-

fidence — predated the new rules.

“People give us their grandkids’ and great-grandkids’ money.

So they really need to be able to trust us,” Monteiro says. “Trust

is one of those things that is very hard to build and very easy to

lose, as we have all seen in recent events in the media.”

Monteiro says Sun Life follows a policy of providing value for

whatever data clients share. The company’s digital benefits as-

sistant, called Ella, is an example. Ella uses a set of predictive

models to “nudge” clients to take actions that are in their best

interest, such as maximizing retirement contributions (if fi-

nancial data shows the client is not) and using ancillary health

care services (like a chiropractor or wellness program) that can

improve the person’s quality of life based on, for example, the

stress inherent in the person’s job. The firm’s satisfaction scores

increase significantly — 27 points in a Net Promoter Score scale

— when Ella engages proactively with these clients, he says.

Health care is an industry in which leaders have experience in

guarding customer data. Dr. Timothy Crone, medical director,

business intelligence and enterprise analytics, at the Cleveland

Clinic, says the organization takes a cautious approach with

its data management, even as opportunities to share data with

outside parties promise to yield new health insights that could

benefit patients — and even as some enthusiastic patients want

to share more of their data.

Why? Because, as Crone explains, even if patients trust health

care providers more than some other businesses that make

headlines with data breaches, “I think that, at this point, that’s

a privilege we still have the opportunity to lose.”

That is a prudent approach. What the new GDPR requirements

are effectively doing is pushing retailers and other companies

to be more like organizations in health care and financial ser-

vices, says Dean Abbott, co-founder and chief data scientist at

SmarterHQ.

“GDPR does very good things for consumers, of course, but

there’s a lot of best practices in the regulations,” Abbott says,

noting that the additional transparency represents a strong

step. It means that data scientists have to be ready to not only

justify their use of customer data in algorithms, but be ready

to communicate how and why they are doing it. The impor-

tance of that transparency and data stewardship only grows as

organizations’ use of analytics evolves to AI by automating the

models and adding learning elements.

And that means analytics professionals need to monitor the

evolving social contract for using data to customers’ benefit.

“We view ourselves as a customer-first company, and we are

nothing without our customers’ success — and then their trust,

ultimately,” says Caterpillar’s Vawter. “And if we’re not creating

value from the data that we’re collecting from them, then we

shouldn’t be using it at all.” l

“We view ourselves as a customer- first company, and we are nothing without our customers’ success — and then their trust, ultimately.” MORGAN VAWTER, CATERPILLAR


13

DataSF Teaches the Art of Asking Analytical Questions


DataSF, the analytics group for the city and county of San Francisco, is teaching people throughout the government to find opportunities for advanced analytics. It’s doing that by teaching local officials how to frame questions that tap data and the answers that can improve public services.

That approach has led to projects such as work with the Department of Public Health to understand what is driving the cost of its mental health programs. DataSF also helped the city administrator’s office find opportunities for fleet vehicle sharing to improve use of vehicles, leading to both lower costs for the city and greener management of the fleet.

“It’s about developing the organizational muscle across all those different service lines to ask questions that are amenable to data science,” explains Joy Bonaguro, who was chief data officer for the city and county of San Francisco at the time she was interviewed for this report. The objective is to help clients within government spot opportunities to use AI and data science within their own verticals.

“We’ve developed something called a project typology that we use to help solicit and define data science questions with our departments. So we don’t say, ‘Hey, do you want to do AI and data science?’ We say, ‘Here are the kinds of questions, and here are a bunch of examples that we can help you answer,’” Bonaguro explains. “If we train our departments to spot data science opportunities, then that’s how we spread it throughout the organization.”

Bonaguro credits the work of others in the public sector, including New Orleans’ NOLA-lytics, which developed an analytics topology that speaks to common business problems. DataSF has posted an online information sheet that categorizes the kinds of problems data science can solve, such as finding the needle in a haystack (figuring out where to direct limited resources), prioritizing a backlog, flagging important items early for action, A/B testing (to find out which communication style works best), and optimizing resources.

GO

VE

RN

ME

NT

Joy Bonaguro, former chief data officer,

city and county of San Francisco

If we train our departments to spot data science opportunities, then that’s how we spread it throughout the organization.”

“


14

Cross-Functional Teamwork Improves Predictive Models at Barclays US


Putting analytics experts shoulder to shoulder with business domain experts doesn’t just build a stronger culture. At Barclays US, it has built better analytics.

The primary focus of analytics at the bank and credit card issuer is the customer journey — getting to the right customer at the right time with the right kind of offer, according to Vishal Morde, vice president of data science and advanced analytics. “If you can actually achieve that, it will make your business more profitable. And your customers will be happy because they’re getting what they’re really looking for.”

A key component of the company’s analytics approach is to tap business domain expertise throughout a project, Morde says.

Barclays US has integrated business people into the analytics process by creating a data sci-ence lab and setting up a cross-functional team that includes business owners. “They were part of the creation of this data science lab,” Morde explains. “Because they’re fairly inte-grated upfront, we could actually now set up this whole environment, set up projects which would actually deliver value and help them to solve the business problem.”

For example, a project to create a better predictive model for determining who will respond to a certain kind of offer included both data scientists and acquisition marketing staff. “The data scientist would say, ‘Hey, I’m looking at this data, and there’s some seasonality to it.’ And a business person will say, ‘Yeah, that makes sense. It has to be the holiday season. That’s where people start responding.’”

Because interactions like this were happening earlier in the process, before data scientists actually produced a model, the teams were able to produce better models more quickly, Morde says. By tapping the consumer insight and some of the anecdotal hypotheses that business experts have, and testing them out with advanced data science methods, Barclays US was able to improve prediction power significantly — in some cases by 50%, he adds.

Wins like that don’t come from just data, methods, or analytics tools, Morde says: “It was that we were able to transfer some of the domain knowledge from the marketing folks into our models. You’re actually incorporating years and years of expert knowledge that people gathered about consumer behavior and consumer needs and wants.”

FIN

AN

CIA

LS

ER

VIC

ES

Vishal Morde, vice president of data

science and advanced analytics, Barclays US

“You’re actually incorporating years and years of expert knowledge that people gathered about consumer behavior and consumer needs and wants.”


Building Trust in Innovation by Creating a Culture of Inquiry and Experimentation

Game-changing insights can result from investments in

data infrastructure, talent, and technology. But simply

generating analytical findings is not the ultimate goal

of the analytics leaders we interviewed for this year’s Data & An-

alytics report. All those investments truly pay off when people

throughout an organization accept those insights and put them

to work to make decisions, redesign business processes, and re-

think strategy. So, what are some tactics used to create a culture

in which people ask questions, seek data and analytics that can

help answer those questions, and then apply the results?

Barclays US appointed an analytics leader for each business unit

and installed cross-functional teams made up of analytics and

business experts in a data science lab. Caterpillar runs an annual

“Analytics Now” conference for stakeholders across the compa-

ny to learn about capabilities and showcase successful projects,

while seeking input and collaboration from an advisory council

of engineers, talent managers, and other domain experts. The

Cleveland Clinic has set up its own council, with an open door to

participate and provide feedback on its analytics program.

Our survey investigated a range of practices associated with

building a culture of analytics, such as actions and messaging

from leadership, workforce data literacy efforts, and organiza-

tional choices. On the whole, those who report the most activity

on these fronts are also more likely to exhibit the most trust

in data and analytics and be on the winning side of the “utility

gap,” meaning they not only have access to data but have the

right data to inform their business decisions.

Two organizational factors emerged that may have a bearing

on driving analytics maturity:

1. One is having a clear leader for the company’s analytics

effort: 33% of those who report that their organization has a chief

data officer or a chief analytics officer are more likely to say they

frequently or always have the data they need for decisions. The

same correlation is present for the 39% of respondents who report

that their organizations have centralized data analytics functions.

(Despite this finding, one size may not fit all: Some analytics

leaders find a distributed approach is right for their enterprise.)

2. The survey also found that in many organizations, leaders

are playing an important role in using and championing analyt-

ics. However, leaders’ actions may be slightly louder than their

words: They appear to be more likely to seek out data and use it

in decision-making than to champion analytics or credit it with

delivering business results (see Figure 10). Leadership support is

also softer when it comes to prioritizing investment in analytics.


11%25%

33%21%

9%

13%29%

30%20%

8%

19%32%

30%15%

4%

17%36%

33%11%

3%

19%40%

29%

3%9%

3%9%

16%40%

32%

Prioritize investments in analytics tools

Credit positive business outcomes

to analytics in internal messages

or presentations

Champion the value and use of analytics

Incorporate data and analytics in

decision-making

Seek data and analytics support

decisions

Understand insights presented

by analytics specialists

Figure 10: Leaders Set the Tone for Analytics Adoption

How often do leaders:

Leaders at the majority of companies often champion the value of data and actively seek to apply it when making decisions.



Prioritize investments in analytics tools

Credit positive business outcomes

to analytics in internal messages

or presentations

Champion the value and use of

analytics

Incorporate data and analytics in

decision-making

Seek data and analytics

to support decisions

Understand insights

presented by analytics

specialists

15


Driving Data Literacy Through the Workforce

One area where leadership might do more is analytics skills in

the workforce: This was cited as a top challenge to innovating

by 39% of respondents, second only to competition from other

priorities. We found that while a range of efforts to build data

literacy in the workforce is at least in the planning stages at a

majority of responding organizations, fewer than one in five are

actively engaged in that activity (see Figure 11). Slightly more

are helping analytics experts build business domain expertise.

Encouraging a data-driven culture means encouraging people

to use the data the business is collecting instead of siloing

it away in the IT department, says Kirk Borne, principal data

scientist and executive advisor at consultancy Booz Allen Ham-

ilton. “This culture of experimentation supports an innovation

culture in a business. It’s where people have the freedom to

ask, ‘Given our data, how can we do better?’” he says.

One example of bringing data literacy to the front lines of busi-

ness comes from Jeanne Ross, a principal research scientist at

the MIT Center for Information Systems Research.

The center studied the 7-Eleven convenience store chain in

Japan, which employed counselors to help 200,000 salespeo-

ple interpret and learn from the data the company collected

about daily sales. Sales staff know a key success factor is their

shop’s ability to have rapid inventory turnover, so they monitor

sales of different items in their assigned section of the store.

Counselors visited the stores to teach the salespeople how to

think analytically, using data about sales of different items in

recent days compared to the year earlier and compared to days

with similar weather patterns.

“The counselors say to them, ‘So, what did you hypothesize

about what you’d sell last week?’ They know the answer to the

question because it’s what they ordered,” Ross says. “Then they

say, ‘How did you do? How did your hypotheses turn out?’ Well,

they have the answer to that question sitting in front of them,

too. They have the data.” Then the counselors and sales staff

discuss strategies for improving sales.

Fostering Collaboration Drives Culture Change

It’s when the analytics expert meets the business domain and

true collaboration ensues that the culture really begins to

transform, according to virtually all the practitioners we inter-

viewed for this report. Interestingly, our survey respondents

“Encouraging a data-driven culture means encouraging people to use the data the business is collecting instead of siloing it away in the IT department.” KIRK BORNE, BOOZ ALLEN HAMILTON

Currently do this Implementing

17%18%

19%18%

21%19%

15%16%

17%18%

30%19%

16%14%

17%19%

22%20%

15%

Line-of-business experts receive

training or immersion in

analytics

Analytics specialists receive

training or immersion in

operational areas

Training programs are widely available to develop data and

analytical skills

Workforce data literacy is regularly

assessed

Internal messaging promotes data

literacy as a valued skill

Planning

Considering No activity

30%

29%

17%

35%

17%25%

Many organizations have an opportunity to do more to tackle the analytics skills shortage. The good news is that a majority are taking action to build a data-driven workforce, with programs either running or in the planning or implementation stages.


Figure 11: Educate to Innovate


Line-of-business experts receive

training or immersion in analytics

Analytics specialists receive training or immersion in

operational areas

Training programs are widely available to develop data and

analytical skills

Workforce data literacy is regularly

assessed

Internal messaging promotes data

literacy as a valued skill

16


17

by a slight margin point to individuals and teams in operating

units as being most likely to drive innovation with emerging

technologies, ahead of top leadership and even IT (see Figure

12). That underscores why building ties with the business may

be a winning strategy for analytics leaders.

Establishing and maintaining a culture that embraces analytics

starts with identifying opportunities and use cases that will

make a difference.

At Airbnb, a platform that has disrupted the hospitality indus-

try, everything begins with analytics and a strong top-down

data culture, according to Yash Kandyala, head of global busi-

ness analytics for community support.

Nonetheless, Kandyala still needs to have the right data and en-

gage the right people in order to find opportunities for analyt-

ics to drive strategy. At Airbnb, it’s a multidirectional dynamic.

Sometimes colleagues come with questions and want answers.

At other times, his team may notice an important insight in the

data and seek to gain executives’ attention and support.

Kandyala says gaining influence for analytics insights gen-

erated by his team can be challenging: “You may have a very

cool idea, but if it’s not tying back to key stakeholders’ goals or

strategy, then there’s going to be very little attention paid to

it.” He seeks to identify projects that can help drive important

goals for the company. “That can go a long way to create those

opportunities and projects which will be based on analytics as

the backbone and that are tied to a business outcome.”

Analytics Expertise: Centralize vs. Decentralize

As noted, centralization isn’t for everyone. At Sun Life Financial,

embedding analytics capabilities in the business rather than

employing a centralized analytics function ensures strategic

alignment and transparency.

“The idea of centralizing and creating a big function and putting

a lot of money in it centrally is very alluring, because it sounds

like you are going to solve all of your data problems,” says Eric

Monteiro, senior vice president of client solutions. “The benefit

of not having done that is that it’s pretty visible to us what the

impact is when the analytics is with the business and for the

business, and linked into the processes that are required.”

Sun Life analytics and business leaders have codified their col-

laboration on a case-by-case basis. Every analytics program

requires developing an ROI case and winning budget approval from

business decision-makers before it starts. There are also regular

progress reports with business owners to check on results.

Despite the decentralized effort, Monteiro says results can

grow from one project into a major operational area. That’s how

the company’s digital benefits assistant started. It was origi-

nally an analytics experiment to personalize recommendations

for customers, such as tips on retirement contributions. It’s

been so useful that it’s become a major undertaking. “We’ve got

dozens of people that work on it, and it’s a digital channel and

reports like any distribution channel,” he says.

Figure 12: Innovation Is Often a Grassroots Effort

Individuals close to specific business needs are often the drivers of innovation around emerging technologies.

Who is most likely to champion the use of emerging technologies such as AI/machine learning, internet of things (IoT), and blockchain?

24%Top leadership

26%

9%

13%

23%

5%

Individual/teamsin operating units

Marketing

R&D

IT

Other


18

There is a challenge associated with the lack of a centralized

analytics function, Monteiro adds: what to do when there is a

major investment required that goes beyond a typical project.

For example, Sun Life is looking to rearchitect some of its data

so it can move to the cloud, which requires more investment in

architecture and infrastructure. “The incrementalism is a risk

that we have to manage,” he says.

Communication and Education Encourage an Analytics Mindset

Advancing a culture of innovation requires not just being heard

but listening. Joy Bonaguro, who was chief data officer for the

city and county of San Francisco at the time she was inter-

viewed for this report, says much of the work of data science

is about culture and change management, helping internal cli-

ents understand the value of data and how it can lead to new

and innovative ways of thinking. A

key element of the effort is making

sure that data scientists listen.

“The way we’ve approached cul-

ture change, driving data use in the

city, has been to really spend time

to understand the challenges and

barriers to using data across the

city. Then, let’s shape our services

around that so people feel heard,”

Bonaguro says. “And when people

feel heard, then they start to trust

you and they want to work with you

on new things, including things that

are maybe a little riskier or scarier,

like data science projects.”

Getting to that point came after a

number of efforts, including pro-

viding training via the organiza-

tion’s Data Academy (see Figure 13),

demonstration projects like dash-

boards, and the launch of an open

data portal, Bonaguro adds. Over

time, through these educational efforts and by having data

experts working side by side with staff, the agency’s internal

clients have gained the tools to identify the appropriate data

science projects for them and then apply for data science

resources. This approach also helps analytics professionals,

who serve such a diversity of internal customers that they

can’t also be domain experts — Bonaguro points out that San

Francisco’s departments run from “A to Z, an airport to a zoo.”

One success was a DataSF project with the county Department

of Public Health to investigate why participation rates had

fallen by 16% among mothers and babies in the federal Wom-

en, Infants, and Children (WIC) nutrition program from 2011

to 2017. After analyzing six years of data about participation,

program staff interactions with mothers, payments issued, and

Strongly Agree

DataSF's Data Academy Assessment: Leaders Set the Tone for Analytics Adoption

Agree Neither Agree nor Disagree Disagree Strongly Disagree

32% 48% 15%

0% 20% 40% 60% 80% 100%

18% 28% 23%

0% 20% 40% 60% 80% 100%

Daily Weekly Monthly Rarely Never

22% 9%

Figure 13: DataSF’s Data Academy Assessment

Do you feel that your skills improved after taking this Data Academy course?

How often do you use the information or skills you learned in your own work?

DataSF, the analytics group for the city and county of San Francisco, is expanding data literacy throughout the agencies it serves via its Data Academy. It shares success metrics — such as attendees’ assessments of skills gained and how frequently those are applied — via a public dashboard.


19

demographic details, the team developed a model that showed

certain families were more likely to drop out. The results

prompted San Francisco’s WIC program to change its outreach

efforts to encourage more participation.

Such collaborations lead to high-quality results and real con-

nections with internal customers because both parties are in-

vesting in the outcome. “This is where the model of the data

scientist getting the data and disappearing off into a cave and

resulting in some magical, perfect model is a terrible mindset,”

she says. “We set the expectations with our clients that this is

going to take time and you’re really going to have to engage.”

And it also serves to educate people who are not data experts

about the work of analytics and its potential impact — and builds

trust in data scientists’ work as they collaborate to find insights.

Bonaguro’s data science shop serves San Francisco’s range of

“airport to zoo” departments with a tight team of just five full-

time people, with at most three working on the data science

service. But the same collaborative principles apply at larger en-

terprises like Barclays US, Caterpillar, and the Cleveland Clinic.

Vishal Morde, vice president of data science and advanced

analytics at Barclays US, says the company’s data science lab

includes business experts who work with analytics experts to

set up projects to solve business problems. “Both sides need to

come together; both sides need to actually make sure that they

understand each other’s perspective,” Morde says. “They un-

derstand each of the challenges and come up with a more kind

of unified approach, rather than working in silos and saying

that, ‘Oh, somebody else needs to do this job for me.’”

At Caterpillar, culture-building takes the form of ongoing

training sessions like “How to Be an Analytics Champion in the

Business” as well as its annual analytics conference, says Mor-

gan Vawter, chief analytics director. “We certainly understand

not everybody wants to be an analytics professional, nor do we

need them to be. But we do want to help them to understand

the power of the data that they already have access to, and so

we provide a lot of training around that,” Vawter says.

The Cleveland Clinic takes a similar tack. In addition to mak-

ing its advisory council open to all, its analytics organization is

identifying opportunities to collaborate with different groups

throughout the enterprise, says Chris Donovan, executive di-

rector of enterprise information management and analytics.

“We’re very focused on delivering value and specific projects,

and making efforts that we have key sponsors for across the

organization. So, as we lay out our road map, we try to make

sure that we’re working with our cardiovascular institute in

this space. We’re working with our cancer institute in this

space, and our strategy team and our marketing team,” Dono-

van says. Showing each group what his team can do builds sup-

port: “That really engages those folks to be champions for the

program and creates this virtuous cycle of where they recog-

nize the challenges around data, and understanding the need

for governance. And it creates a nice feedback loop for us.”

To a veteran of data science discussions like Eric Siegel, the

efforts and experiences of companies like these demonstrate

how far the field has come. Siegel, author of Predictive Analyt-

ics: The Power to Predict Who Will Click, Buy, Lie, or Die, says

technical experts have always faced challenges in communi-

cating the value of their work to executives, winning approv-

al for projects, and convincing their colleagues to trust and

actually use the results generated by core analytics. What’s

changed are the types of champions he sees rising nowadays to

speak about their work at industry conferences he organizes,

such as Predictive Analytics World and Deep Learning World.

Increasingly, C-suite executives, vice presidents, and directors

of business functions are contributing their voices and exper-

tise to events in the analytics arena, Siegel says. In other words,

leaders whose job titles don’t have technology or analytics in

them are making the case for analytics — and building support

for a data-driven culture. l


20

About the Research

MIT SMR Connections conducted an online survey of MIT Sloan Management Review subscribers in May 2018, drawing 2,413

respondents. Respondents represent a broad range of functions and industries, with more than 70% identifying themselves as

being in management, C-suite, or board roles. The survey included respondents from all regions of the world.

To provide a rich context for discussion of the quantitative research results, we interviewed analytics experts including practi-

tioners, consultants, and academics. These individuals provided insight into good practices and examples of the steps leading

organizations are taking to advance analytical maturity and build data-driven cultures.

Dean Abbott, co-founder and chief data scientist, SmarterHQ

Joy Bonaguro, former chief data officer, city and county of San Francisco

Kirk Borne, principal data scientist and executive adviser, Booz Allen Hamilton

Timothy Crone, MD, medical director, business intelligence and enterprise analytics, Cleveland Clinic

Chris Donovan, executive director of enterprise information management and analytics, Cleveland Clinic

Susan Etlinger, industry analyst, Altimeter Group

Caroline Viola Fry, PhD candidate, MIT Sloan School of Management

Michael S. Goldberg, independent writer and researcher

Christina Hoy, vice president, corporate business information and analytics, Workplace Safety and Insurance Board Ontario

Yash Kandyala, head of global business analytics for community support, Airbnb

David Loshin, principal consultant, Knowledge Integrity

Eric Monteiro, senior vice president of client solutions, Sun Life Financial Canada

Vishal Morde, vice president of data science and advanced analytics, Barclays US

Jeanne Ross, principal research scientist, MIT Sloan Center for Information Systems Research

Beatriz Sanz Saiz, global data and analytics leader for Advisory Services, EY

Eric Siegel, author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, and founder, Predictive Analytics World conference

Morgan Vawter, chief analytics director, Caterpillar

Acknowledgments


21

The annual MIT Sloan Management Review Custom Data & Analytics survey highlights how the most analytically innovative organizations are implementing new tech-

nologies — most recently, AI-based techniques like machine learning and natural language processing.

This latest report focuses on an aspect of data and analytics that hits home for SAS, not just from a technical standpoint, but from an emotional one: trust.

To rely on analytics for decision-making, you have to trust your data. Successful organizations also must trust in their ability to innovate and create a culture where asking questions is welcomed and encouraged.

Then there is the external trust factor — highlighted with the European Union’s recent implementation of the General Data Protection Regulation — in which security and privacy con-cerns are critical for gaining and keeping customers’ faith that you’ll do right by their data.

So why does that connect with us here at SAS? Our mission is “to empower and inspire with the most trusted analytics.” And for more than 40 years, our purpose has been to use curiosity as the driving force behind progress.

Not surprisingly, organizations that have advanced their analytics practice to apply machine learning, AI, and automated analytics to workflows have a similar mindset.

They are more likely to take actions to build data quality, data security, and a culture of data-driven innovation. With reliable data, these analytically mature organizations can move beyond basic BI and dashboards.

The “robust majority” are getting their security measures in order to foster external trust. Privacy efforts, while lagging slightly behind security, are also a priority — obviously driven in part by GDPR compliance.

Eventually, this is another place where data management and AI will meet, with organizations trusting their data and analytics enough to make them part of the foundation for protecting other data.

But none of this happens unless company culture supports it, which could mean anything from hiring a chief data officer or chief analytics officer and having other leaders champion analytics to building skills and strategy with existing talent.

All in all, the results of the survey demonstrate a truth we’ve always believed in: Technology and trust go hand in hand, especially when it comes to the future of data.

Why Advancing Technology Demands Building Trust

Randy Guard, executive vice president and chief marketing officer, SAS

S P O N S O R ’ S V I E W P O I N T

About SASThrough innovative analytics, business intelligence, and data management software and services, SAS helps customers make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®.

To learn more about how technology and trust go hand in hand, visit us at www.SAS.com/innovation.

data, analytics, & ai: how trust delivers value · analytics practices to incorporate ai-based...

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